PROJECT SUMMARY Antinuclear antibodies (ANA) are antibodies that react against self-antigens and are commonly used to help diagnose systemic lupus erythematosus (SLE). Because the test is positive (ANA+) in almost every patient with SLE? even years before the disease onset?a positive ANA test is considered virtually a requisite for the diagnosis of SLE. However, the test is also positive in a large proportion of the general population (~20%). Although very few of these ANA+ individuals will develop an autoimmune disease in the future, the clinical impact of a positive ANA in people without autoimmune disease is unknown. A second problem is that the common occurrence of ANA+ in people without autoimmune disease can lead to the problem of an incorrect diagnosis of SLE, particularly if an ANA+ person also has joint or muscle pain. To more accurately diagnose SLE and prevent false diagnoses, we need to address two major knowledge gaps: 1) we need to understand the importance of a positive ANA test in people without an autoimmune disease; and 2) we need to be able to predict which people with a positive ANA test have or will develop SLE. In this study we will evaluate the overarching hypothesis that clinical and genetic information can: 1) define the clinical consequences of positive ANA in people without autoimmune diseases, and 2) improve risk prediction to differentiate people with increased risk of SLE. Thus, we proposed three Specific Aims: 1) test the hypothesis that a positive ANA in people without autoimmune disease is associated with clinical phenotypes (using a clinical and a genetic approach); 2) test the hypothesis that a clinical prediction model will accurately discriminate patients with early SLE or who are at risk for SLE from among those with positive ANA without an autoimmune disease; and 3) test the hypothesis that the combination of genetic and clinical information will accurately discriminate patients at risk for SLE. To address these aims, we will use the Vanderbilt University Medical Center Biobank (BioVU) and de-identified electronic health records (EHR) to create genetic and clinical risk scores using state-of-the-art genetic techniques and data-driven prediction tools. The results of these studies could: a) define whether people with a positive ANA and no autoimmune disease have an altered risk of illnesses that could be used to guide health-care decisions; and b) transform the care of SLE by improving the accuracy of early-stage SLE diagnosis and identify patients at highest future risk. These findings will help clinicians start treatment earlier to control inflammation and prevent damage and will decrease the rates of misdiagnosis, thereby protecting patients from unnecessary therapies and their side effects.